1. Field of the Invention
The present invention relates to medical devices, which provide a mechanism to sense physiological signals from nerves and muscles in humans. Specifically, the current invention relates to sensing, processing and feature extraction of physiological signals in their pristine form while avoiding error sources arising from electrical noise, signal amplitude variations, DC drift, and filtering.
2. Description of the Related Art
Electrical Noise
In an exemplary case of electrical sensing and amplifying of physiological signals, the amplifier has competing electromagnetic signal sources that may cause deterioration of signal quality performance. Established methods use common mode rejecting amplifier designs, which reference the leads of a signal pair to a reference and a real or virtual ground. When the signals have amplitudes in the range of a few tens of mV, the performance of such solutions is good, as the operating voltage range is many orders of magnitude greater than the supplied signal. On the other hand, for biological signals encountered in electrocardiography (ECG) and electroencephalography (EEG), the traditional techniques with an external ground are not optimal as the relatively smaller magnitude of the biological signals can be easily overwhelmed by noise.
In a conventional data acquisition system, the input bandwidth must be limited to avoid aliasing. Aliasing is the result of not having sufficient data samples available to distinguish a component with frequency content F from one with n×2F. However, aliasing would become an issue only if sufficient energy is contained in higher frequencies. According to the Shannon/Nyquist theorem, the sample frequency must be at least twice the lowest frequency component contained in the signal at the lowest amplitude of the dynamic range of the system.
The frequency range for ECG signals has traditionally included the line frequencies, 50 Hz and 60 Hz. In a traditional system, with an input pair and a common ground in an office, home or industrial environment, there is likely considerable line frequency content in the input signal, at the input amplifier and/or sampling location. One solution would include a notch filter for 50 Hz and 60 Hz, or one broad band enough to filter out the band from 45-65 Hz. By Shannon/Nyquist, the notch filtering will introduce a non-linear effect from at least 22.5 Hz to 130 Hz resulting in system sensitivity reduction. Even a high Q filter will not avoid this issue. The other common line frequency for aviation and marine equipment is 400 Hz. However, this is generally high enough not to affect ECG signals. If there is no meaningful information contained in the filtered out band, there will not be any adverse issues with the filtering approach. In practical applications, that is almost never the case. Since important information is contained in those frequency bands, there is a need for a technique that includes the entire band from 10 Hz to 200 Hz so pristine biological signals can be acquired.
Another problem afflicting present-day devices relates to the rejection of amplitude modulated or burst electromagnetic fields. One source of burst line frequency noise is faulty, or poorly designed, appliances where the patient is in close proximity of or in contact with a line frequency AC powered device. The patient actually is part of a direct or induced electrical pathway to ground. In contrast, to sense detection in the presence of continuous additive line frequency interference, the operation of the sensing circuit during amplitude modulated or burst electromagnetic interference (EMI) is probably more important to patient safety. Burst line frequency noise is a potentially dangerous situation for pacemaker-dependent patients because burst noise may inhibit stimulus generation in a cardiac control device. The potential hazard of continuous line frequency noise, in comparison to burst noise, is less precarious because continuous line noise will cause the device to pace asynchronously with respect to a spontaneous cardiac rate, but the device will still support the patient.
A further problem with prior art techniques is the usage of digital or active analog filtering in the front end circuit that is directly connected to electrodes. This exposes the internal circuits to the full noise amplitude and has the risk of running out of “dynamic range.” For example, if the amplifier output hits the rails (ground or supply), it is no longer linear, or amplifying. For example, given a normal signal range of 1-10 mV, a gain of 200, a noise burst of 100 mV and a 5 volt supply rail, the output amplitude of the true signal is 200 mV to 1.0 volt and the noise signal in the output is 20 volts, which is well beyond the supply rail voltage. The amplifier may simply peg at the rail, or oscillate between ground and a supply voltage level, without linear relation to the input signal.
Yet another problem with prior techniques relates to difficulty in cardiac monitoring when attempting to segregate electrical noise (EMI) from fibrillation. The QRS complex is high bandwidth (50-500 Hz), with conventional methods showing 2-60 Hz and an inability to detect the characteristic high slew rate QR complex (about 50-100 μsec for min to max−about a 10-20 mV amplitude) Standard systems require high gain (500×) to get to a reasonable 1.0 V pp signal, as the ECG amplitude is documented as a 2-10 mV signal. However, the composition at 50-500 Hz is very different. The highest amplitudes are the shortest duration, and classic low BW filtering reduces these to slower, lower amplitude. This difficulty in differentiating EMI from QRS manifests in cardiac monitoring and rhythm diagnosis both on surface ECG and internally in pacemakers and defibrillators. The potential for unnecessary shocks from implantable defibrillators makes the internal case particularly significant. Improved noise immunity is needed.
Signal Amplitude Variations, DC Drift, and Filtering and their Effects on Signal Transition Detection and Feature Extraction
Medical devices often require signal processing based on signal transition detection for the purpose of feature extraction. The results of feature extraction on physiological signals may be used to discern the exact nature of the underlying physiological processes, in some cases even enabling autonomous actions by electronic instruments embedded within a human (e.g. pacemaker and/or defibrillator).
In an exemplary case, established methods use detection of signal transitions as the starting point for feature extraction, Variations in signal amplitudes, and superposition of DC drift upon the signal, may introduce significant errors into signal transition detection, thereby potentially adversely impacting the ultimate decision making resulting from feature extraction.
Transition detection has been conventionally accomplished by detecting signal zero-crossings. However, any low frequency contamination of the signal may cause the “baseline” or “the zero line” to wander, thereby compromising the accuracy of zero crossing detection. In this case, the signal may be prevented from crossing the baseline as a result of low frequency content. To address this, one solution has been to amplify the signal into a fixed amplitude limit, thereby removing the amplitude information before applying the zero crossing detection. The result is a “band limited signal” that does not contain any valid signal components above or below cutoff frequencies of a pass band. Nevertheless, a band limited signal contains low amplitude components from the stop bands, i.e. frequencies above or below the pass band, or noise. The low frequency content would still be prevalent and cause inaccuracies in signal detection. Such noise may cause erroneous detection of zero-crossings. Additionally, removing the amplitude information in this way precludes later re-production of the original signal.
Other current signal processing methodologies perform band pass filtering and compression of the signal to minimize dynamic range, and then pass the result through a signal transition detector. Signal amplitude compression tends to produce a constant amplitude signal, or at least one with minimal dynamic range. Therefore a desired detector would be amplitude independent, and thus not directly be affected by band pass filtering controlling amplitude.
In the cardiac arena, a problem with prior-art cardiac monitoring systems is difficulty differentiating between the QRS and T waves. In reality the two are quite different: QRS is high frequency, short duration, whereas T wave is low frequency, long duration. In traditional systems these sometimes appear to have similar (20.50%) levels in amplitude and appear ‘rounded’. Comparison of the real signals shows no such similarity (the T wave is <10% of the QRS). Mistaking a T wave for another heart beat could produce a double heart rate, and subsequent misinterpretation for ventricular tachycardia. Current systems use a “lock out” for the T wave complex, to avoid mis-detecting it, assuming that a steady heart beat is normally available and serves to more or less ‘predict’ where the next beat should be. It's called a ‘lock-out’ feature. This difficulty in differentiating QRS from T manifests in cardiac monitoring and rhythm diagnosis both on external ECG and internally in pacemakers and defibrillators, The potential for unnecessary shocks from implantable defibrillators makes the internal case particularly significant. An improved signal transition detector would alleviate this problem.
Again related to cardiac, there exists a need to identify existing substrates (chronic substrates) in cardiac muscle which could cause serious rhythm abnormalities such as ventricular tachycardia. The prior-art demonstrates two established methods (T wave alternans and Signal averaged ECG). Both of these methods require signal averaging and amplification because of the necessary filtering of the current techniques to remove EMI. An improved signal transition detector is needed to provide a superiorly pure signal, thereby alleviating the necessity of signal averaging.
Further related to cardiac, there also exists a need to identify real time changing substrates in cardiac muscle which are electrical reflections of mechanical and ischemic (reduced blood supply) changes in the ventricular muscle. If a patient's heart failure is worsening there are going to be changes in mechanical stretch characteristics of the muscle and a high fidelity electrical signal would reflect this mechanical change, as it also would in the event of an ischemic event to the muscle. Prior-art offers no signal transition detection techniques with sufficient fidelity to perform diagnoses based on such detections. An improved signal transition detector is needed to produce signals of such fidelity.
Related to EEG, there exists a need to obtain higher fidelity, less noisy signals. The prior art uses single ended detection (—i.e. the micro-Volt signals are carried in single ended with a ‘common’ usually clipped to an earlobe). Muscle signals are about 50× greater in amplitude than neuronal signals (muscle=10 mV, neuron=0.2 mV). An EEG needs to be devoid of low frequency disturbances, although group wave patterns are from 2-40 Hz. Detail is visible up to several hundreds of hertz, but not currently cataloged due to noise contamination. Noise is tremendous and any muscle noise dominates (eyebrows, eyes, facial, jaw, swallowing). Use of the groundless amplification and first derivative zero detection techniques of this disclosure would greatly enhance EEG signal fidelity and usefulness.
In view of all of the foregoing discussion, there is a need for a system that can amplify biological signals from muscles and/or nerves without concomitantly amplifying the noise. There is also a need for a signal transition detector that is not subject to DC drift in the signal, is not subject to signal amplitude variations, does not lose signals with the usual filtering processes, lends itself amenably to robust feature detection, and allows for reproduction of the original signal but without the DC component.